论文标题
通过学习局部成本功能学习炒扰流器的贫瘠高原
Barren plateaus from learning scramblers with local cost functions
论文作者
论文摘要
贫瘠的高原的存在最近揭示了Quantum机器学习（QML）的新培训挑战。揭示贫瘠高原背后的机制对于理解QML可以有效解决问题的范围至关重要。最近，当学习随机单位的全球性能时，贫瘠的高原已经存在，这在学习黑洞动态时很重要。如果我们希望将QML应用于量子多体系统，确定当地成本功能是否可以规避这些贫瘠的高原是相关的。我们证明了一个无关的定理，表明当地成本功能在学习随机单一属性时会遇到贫瘠的高原。
The existence of barren plateaus has recently revealed new training challenges in quantum machine learning (QML). Uncovering the mechanisms behind barren plateaus is essential in understanding the scope of problems that QML can efficiently tackle. Barren plateaus have recently been shown to exist when learning global properties of random unitaries, which is relevant when learning black hole dynamics. Establishing whether local cost functions can circumvent these barren plateaus is pertinent if we hope to apply QML to quantum many-body systems. We prove a no-go theorem showing that local cost functions encounter barren plateaus in learning random unitary properties.
